Trends and Outlook in Research and Development Activities relating to Train Operation Management and Transport Planning & Marketing

The railway sector is facing major changes due to declining birthrates, an aging society, the intensifi cation of meteorological disasters, and the need to further reduce CO 2 emissions. The spread of COVID-19 since 2020, has had a major impact on the railway business, leading to an urgent need to respond to the drastic changes in demand for mobility in society. Until now, the Signal and Transport Information Technology Division has focused on research and development to further improve the safety and convenience of train operations and reduce the cost and burden of operations related to train operation management. Now, it is necessary to extend research and development to take into account the consequences of COVID-19 by, for example, speeding up our R&D. This paper introduces the purpose and goals of R & D undertaken by the Transport Operation Systems Lab and the Transport Planning and Marketing Lab in FY2020. This paper also describes the R&D concepts and policies for responding to a post-COVID-19 environment. in detail from various perspectives such as user arrival time, train operation frequency, number of transfers, train delays, and operating costs. The revised plans should then be compared with other timetables before a decision is made. However, preparing and evaluating timetable revisions is currently a very time-consuming procedure, which makes it difficult to perform detailed analyses or quantitative comparative evaluations of multiple proposed plans. This R&D project therefore first aimed to establish an evaluation method for timetables, keeping in mind that in the future, automatic proposals and evaluations of multiple schedules should improve the quality and efficiency of operations. Focusing on train delays in particular, a method was constructed to quantitatively evaluate timetables from the perspective of delays [2]. Specifically, the constructed method first identifies the areas affected by the spread of delays at each location, from daily delay record data, and quantitatively evaluates the number of trains and stations in these areas to express the impact of the delay. Then, a method was devised to extract locations which had higher average impact values for a certain period (e.g. 1 month) which would be locations where delay measures should be considered with priority. Finally, a system loaded with this information was developed, to support delay countermeasures (Fig. 1). This has enabled the quan-QR in